Detecting significant genotype–phenotype association rules in bipolar disorder: market research meets complex genetics

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

  • René Breuer, Universität Heidelberg
  • ,
  • Manuel Mattheisen
  • Josef Frank, Universität Heidelberg
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  • Bertram Krumm, Universität Heidelberg
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  • Jens Treutlein, Universität Heidelberg
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  • Layla Kassem, National Institute of Mental Health
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  • Jana Strohmaier, Universität Heidelberg
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  • Stefan Herms, University of Bonn, University Hospital Bonn
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  • Thomas W. Mühleisen, University of Bonn, University Hospital Bonn
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  • Franziska Degenhardt, University of Bonn, University Hospital Bonn
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  • Sven Cichon, University of Bonn, University Hospital Bonn, Forschungszentrum Jülich (FZJ), University Hospital and University of Basel
  • ,
  • Markus M. Nöthen, University of Bonn, University Hospital Bonn
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  • George Karypis, University of Minnesota Twin Cities
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  • John Kelsoe, University of California, San Diego
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  • Tiffany Greenwood, University of California, San Diego, BGI Shenzhen, Shenzhen 518000, China
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  • Caroline Nievergelt, University of California, San Diego
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  • Paul Shilling, University of California, San Diego
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  • Tatyana Shekhtman, University of California, San Diego
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  • Howard Edenberg, Indiana University School of Medicine Indianapolis
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  • David Craig, The Translational Genomics Research Institute
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  • Szabolcs Szelinger, The Translational Genomics Research Institute
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  • John Nurnberger, Indiana University School of Medicine Indianapolis
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  • Elliot Gershon, University of Chicago, Chicago, Illinois.
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  • Ney Alliey-Rodriguez, University of Chicago, Chicago, Illinois.
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  • Peter Zandi, John Hopkins Bloomberg School of Public Health
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  • Fernando Goes, John Hopkins School of Medicine
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  • Nicholas Schork, The Translational Genomics Research Institute, J. Craig Venter Institute
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  • Erin Smith, Scripps Genomic Medicine & The Scripps Translational Sciences Institute (STSI), University of California, San Diego, School of Medicine
  • ,
  • Daniel Koller, Indiana University School of Medicine Indianapolis
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  • Peng Zhang, University of Michigan
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  • Judith Badner, University of Chicago, Chicago, Illinois.
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  • Wade Berrettini, The Pennsylvania State University
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  • Cinnamon Bloss, University of California, San Diego
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  • William Byerley, University of California, San Francisco
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  • William Coryell, University of Iowa Hospitals and Clinics
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  • Tatiana Foroud, Indiana University School of Medicine Indianapolis
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  • Yirin Guo, Children’s Hospital of Philadelphia
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  • Maria Hipolito, Howard University Hospital
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  • Brendan Keating, University of Pennsylvania, School of Medicine
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  • William Lawson, University of Texas, Austin, Texas
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  • Chunyu Liu, University of Chicago, Chicago, Illinois.
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  • Pamela Mahon, John Hopkins School of Medicine
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  • Melvin McInnis, University of Michigan, Ann Arbor, Michigan.
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  • Sarah Murray, Scripps Genomic Medicine & The Scripps Translational Sciences Institute (STSI), University of California, San Diego
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  • Evaristus Nwulia, University of Texas, Austin, Texas
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  • James Potash, University of Iowa School of Medicine
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  • John Rice, Internal Medicine, Washington University in St. Louis, School of Medicine
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  • William Scheftner, Rush University Medical Center
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  • Sebastian Zöllner, University of Michigan, Ann Arbor, Michigan.
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  • Francis J. McMahon, National Institute of Mental Health
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  • Marcella Rietschel, Universität Heidelberg
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  • Thomas G. Schulze, Universität Heidelberg, National Institute of Mental Health, University of Göttingen, Ludwig-Maximilians-University München

Background: Disentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide association studies (GWAS) have been performed. Similar to other complex disorders, major breakthroughs in explaining the high heritability of BD through GWAS have remained elusive. To overcome this dilemma, genetic research into BD, has embraced a variety of strategies such as the formation of large consortia to increase sample size and sequencing approaches. Here we advocate a complementary approach making use of already existing GWAS data: a novel data mining procedure to identify yet undetected genotype–phenotype relationships. We adapted association rule mining, a data mining technique traditionally used in retail market research, to identify frequent and characteristic genotype patterns showing strong associations to phenotype clusters. We applied this strategy to three independent GWAS datasets from 2835 phenotypically characterized patients with BD. In a discovery step, 20,882 candidate association rules were extracted. Results: Two of these rules—one associated with eating disorder and the other with anxiety—remained significant in an independent dataset after robust correction for multiple testing. Both showed considerable effect sizes (odds ratio ~ 3.4 and 3.0, respectively) and support previously reported molecular biological findings. Conclusion: Our approach detected novel specific genotype–phenotype relationships in BD that were missed by standard analyses like GWAS. While we developed and applied our method within the context of BD gene discovery, it may facilitate identifying highly specific genotype–phenotype relationships in subsets of genome-wide data sets of other complex phenotype with similar epidemiological properties and challenges to gene discovery efforts.

Original languageEnglish
Article number24
JournalInternational Journal of Bipolar Disorders
Volume6
Issue1
Number of pages10
ISSN2194-7511
DOIs
Publication statusPublished - 1 Dec 2018

    Research areas

  • Bipolar disorder, Data mining, Genotype–phenotype patterns, Rule discovery, Subphenotypes

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